Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/brain-bootstrap/claude-code-brain-bootstrap/lintnpx skills add brain-bootstrap/claude-code-brain-bootstrap --skill lintgit clone --depth 1 https://github.com/brain-bootstrap/claude-code-brain-bootstrapWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00030 | $0.00445 |
| Opus 5 | $0.00015 | $0.00222 |
| Sonnet 5 | $0.00006 | $0.00089 |
| Haiku 4.5 | $0.00003 | $0.00044 |
Grade A, and why
lint scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Lint Skill
Run linter and formatter, fix auto-fixable issues.
Protocol
1. Read lint command
Read claude/build.md for the lint and format commands.
2. Run formatter check (non-destructive)
# Example: npx biome check . 2>&1 | tail -30
# Example: black --check . 2>&1 | tail -20
3. Run linter
# Example: npx eslint . --max-warnings 0 2>&1 | tail -40
# Example: ruff check . 2>&1 | tail -30
4. Auto-fix safe issues
# Example: npx biome check --write . 2>&1 | tail -20
# Example: black . && ruff check --fix . 2>&1 | tail -20
5. Review remaining issues
For issues that can't be auto-fixed:
- Understand the rule being violated
- Fix the root cause (not by disabling the rule)
- Only disable a rule if you understand exactly why it's a false positive
6. Re-run to confirm clean
# Run the same check commands again — must exit 0
7. Report
Lint: PASS — 0 errors, 0 warnings
Format: PASS
or
Lint: FAIL — 3 errors, 5 warnings
- error: ... (fixed)
- warning: ... (fixed)
Remaining: 0 (all resolved)
Rules
- NEVER suppress a lint rule without a written justification in a comment
- NEVER "fix" lint by converting errors to warnings
- Pre-existing lint issues: document them, do NOT fix unrelated issues
- Only fix lint issues introduced by the current change (unless asked to do a lint pass)
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 62 lines · 30 tokens per session scan A 3d9fa2ce2491
lint is a skill published in the GitHub repository brain-bootstrap/claude-code-brain-bootstrap (11 stars, last pushed 4mo ago), licensed MIT. It adds 30 tokens to every session and 445 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
implement
TRIGGER when: user asks to implement, fix, build, or work on something — whether from a docs/wip plan OR a standalone task (bug fix, GitHub issue, one-off change). Examples: "work on task 1", "fix this bug", "implement feature X from the issue". Provides structured execution with profile detection, dependency…
review-spec
Use after implementing tasks or mid-feature to verify code matches design docs and ensure they are in sync. Detects spec deviations, missing implementations, doc inconsistencies, and outdated docs in design and implementation documentation.
chain-of-verification
Apply Chain-of-Verification (CoVe) prompting to improve response accuracy through self-verification. Use when complex questions require fact-checking, technical accuracy, or multi-step reasoning.
review-code
Code review of current git changes with an expert senior-engineer lens. Detects SOLID violations, security risks, and proposes actionable improvements. Use when performing code reviews.
review-design
Review design, implementation, and task documents produced by design. Evaluates document quality, internal consistency, and technical soundness. Use after design completes and before starting implement.
design
Use in pre-implementation (idea-to-design) stages to understand spec/requirements and create a correct implementation plan before writing actual code. Turns ideas into a fully-formed PRD/design/specification and implementation-plan. Creates design docs and task lists in docs/wip/.